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Skeletal sequence data, as a widely employed representation of human actions, are crucial in Human Activity Recognition (HAR).
Semantics-guided neural networks for efficient skeleton-based human action recognition
Pengfei Zhang, Cuiling Lan, Wenjun Zeng, Jianru Xue, and Nanning Zheng · 1904
Earlier work this paper cites.
Mocap database hdm05
Meinard Müller, Tido Röder, Michael Clausen, Bernhard Eberhardt, Björn Krüger, and Andreas Weber · 2007
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Bayesian learning via stochastic gradient langevin dynamics
Max Welling and Yee W Teh · 2011
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Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
Earlier work this paper cites.
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Hierarchical recurrent neural network for skeleton based action recognition
Yong Du, Wei Wang, and Liang Wang · 2015
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Realtime style transfer for unlabeled heterogeneous human motion
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Variational inference: A review for statisticians
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Earlier work this paper cites.
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Laura F Bringmann, Ellen L Hamaker, Daniel E Vigo, André Aubert, Denny Borsboom, and Francis Tuerlinckx · 2017
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Averaging weights leads to wider optima and better generalization
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Spatial temporal graph convolutional networks for skeleton-based action recognition
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Ntu rgb+ d 120: A large-scale benchmark for 3d human activity understanding
Jun Liu, Amir Shahroudy, Mauricio Perez, Gang Wang, Ling-Yu Duan, and Alex C Kot · 2019
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A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 2019
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Improving transferability of adversarial examples with input diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou, Song Bai, Jianyu Wang, Zhou Ren, and Alan L Yuille · 2019
Skeleton-based human action recognition via convolutional neural networks (cnn)
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Human skeletons and change detection for efficient violence detection in surveillance videos
Guillermo Garcia-Cobo and Juan C SanMiguel · 2023
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Boosting adversarial transferability by achieving flat local maxima
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Channel-wise topology refinement graph convolution for skeleton-based action recognition
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Making substitute models more bayesian can enhance transferability of adversarial examples
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Defending black-box skeleton-based human activity classifiers
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